Holcomb, J. | Search bioRxiv

bioRxiv2025

MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJs platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging. MotivationCalcium imaging has become one of the most common methods for investigating neural activity, however analytical methods are limited to a few software platforms or are custom made. This limits replicability and imposes restrictions on incorporating additional tools to support analysis. To address these challenges, we developed a modular, graphical based, open-source toolbox, based in the ImageJ application, for performing functional imaging analysis in diverse models and datasets. HighlightsO_LIDeveloped MCA, an ImageJ based plugin for analyzing functional imaging datasets. C_LIO_LIValidated accuracy of MCA functions C_LIO_LIUtilized MCA across multiple sensory modalities and model organisms C_LI

neuroscience↗

bioRxiv2025

Ralstonia pseudosolanacearum LOV domain protein regulates environmental stress tolerance, iron homeostasis, and bacterial wilt virulence

Ralstonia pseudosolanacearum (Rps), which causes bacterial wilt disease of many crops, must integrate environmental signals to successfully transition from soil to its pathogenic niche in host plant xylem tissue. Mutating a putative sensing/signaling gene had little transcriptomic effect on Rps strain GMI1000 in culture. However, when the mutant grew in tomato over 180 genes were differentially expressed relative to wild type. The gene was therefore named rprR for Ralstonia plant-responsive regulator. In planta, the {Delta}rprR mutant dysregulated genes for diverse traits including stress response, degradation of phenolic compounds, motility, attachment, and production of extracellular polysaccharide (EPS), which is a key bacterial wilt virulence factor. Quantifying Rps EPS by ELISA found increased levels in stems of plants infected with {Delta}rprR as compared to wild type. Functional assays showed {Delta}rprR is defective in attachment to tomato roots, colonization of tomato stems, and bacterial wilt virulence. In rich medium, {Delta}rprR formed biofilm normally, but the mutant formed less biofilm in tomato stem homogenate and in tomato xylem sap under flow. This phenotype correlates with the mutants altered expression of EPS biosynthetic genes and aberrant extracellular matrix. When grown in tomato stem homogenate, {Delta}rprR produced 57% more of the bacterial signal cyclic di-GMP (c-di-GMP) than wild type. This is consistent with the presence in RprR of predicted c-di-GMP modulating domains. Together these findings reveal that RprR, which is highly conserved across plant pathogenic Ralstonia, modulates several bacterial wilt virulence traits in response to the plant host. ImportanceMembers of the Ralstonia solanacearum species complex (RSSC) cause bacterial wilt, a globally destructive disease of market and subsistence crops. Like other plant-associated microbes, bacteria in the RSSC must integrate a complex array of biotic and abiotic signals to successfully infect plant hosts. RSSC genomes all encode an unusual protein, termed RprR, that contains multiple sensing and signaling domains, including two putative modulators of the secondary messenger c-di-GMP. Deleting RprR in Ralstonia pseudosolanacearum had a plant-dependent effect on many traits, including production of the key virulence factors biofilm and exopolysaccharide, as well as intracellular c-di-GMP levels. While c-di-GMP has been investigated in other plant pathogenic bacteria, this is the first report of its role in the RSSC. Most importantly, rprR was required for Ralstonia to effectively colonize plants and cause wilt disease. Thus, RprR is a plant-responsive sensor-regulator that controls pathogen adaptation to the host environment and virulence.

microbiology↗